Overview

Dataset statistics

Number of variables23
Number of observations35040
Missing cells20327
Missing cells (%)2.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.1 MiB
Average record size in memory184.0 B

Variable types

DateTime1
TimeSeries20
Numeric2

Alerts

TA_F_MDS is highly overall correlated with SW_IN_POT and 8 other fieldsHigh correlation
SW_IN_POT is highly overall correlated with TA_F_MDS and 11 other fieldsHigh correlation
SW_IN_F_MDS is highly overall correlated with TA_F_MDS and 11 other fieldsHigh correlation
LW_IN_F is highly overall correlated with SW_IN_POT and 5 other fieldsHigh correlation
LW_IN_JSB_F is highly overall correlated with TA_F_MDS and 1 other fieldsHigh correlation
VPD_ERA is highly overall correlated with TA_F_MDS and 10 other fieldsHigh correlation
P_F is highly overall correlated with LW_IN_FHigh correlation
USTAR is highly overall correlated with SW_IN_POT and 5 other fieldsHigh correlation
RH is highly overall correlated with TA_F_MDS and 10 other fieldsHigh correlation
NETRAD is highly overall correlated with SW_IN_POT and 10 other fieldsHigh correlation
PPFD_IN is highly overall correlated with TA_F_MDS and 11 other fieldsHigh correlation
CO2_F_MDS is highly overall correlated with TA_F_MDS and 3 other fieldsHigh correlation
TS_F_MDS_1 is highly overall correlated with TA_F_MDS and 10 other fieldsHigh correlation
LE_F_MDS is highly overall correlated with TA_F_MDS and 10 other fieldsHigh correlation
H_F_MDS is highly overall correlated with SW_IN_POT and 7 other fieldsHigh correlation
NPP_DT_VUT_USTAR50 is highly overall correlated with SW_IN_POT and 8 other fieldsHigh correlation
Month is highly overall correlated with DoYHigh correlation
DoY is highly overall correlated with MonthHigh correlation
WD has 8593 (24.5%) missing valuesMissing
USTAR has 8593 (24.5%) missing valuesMissing
RH has 1047 (3.0%) missing valuesMissing
NETRAD has 1047 (3.0%) missing valuesMissing
PPFD_IN has 1047 (3.0%) missing valuesMissing
TA_F_MDS is non stationaryNon stationary
SW_IN_POT is non stationaryNon stationary
SW_IN_F_MDS is non stationaryNon stationary
LW_IN_F is non stationaryNon stationary
LW_IN_JSB_F is non stationaryNon stationary
VPD_ERA is non stationaryNon stationary
PA_ERA is non stationaryNon stationary
P_F is non stationaryNon stationary
WS_F is non stationaryNon stationary
RH is non stationaryNon stationary
NETRAD is non stationaryNon stationary
PPFD_IN is non stationaryNon stationary
CO2_F_MDS is non stationaryNon stationary
TS_F_MDS_1 is non stationaryNon stationary
LE_F_MDS is non stationaryNon stationary
H_F_MDS is non stationaryNon stationary
NPP_DT_VUT_USTAR50 is non stationaryNon stationary
Year is non stationaryNon stationary
Month is non stationaryNon stationary
DoY is non stationaryNon stationary
TA_F_MDS is seasonalSeasonal
SW_IN_POT is seasonalSeasonal
SW_IN_F_MDS is seasonalSeasonal
LW_IN_F is seasonalSeasonal
LW_IN_JSB_F is seasonalSeasonal
VPD_ERA is seasonalSeasonal
PA_ERA is seasonalSeasonal
P_F is seasonalSeasonal
WS_F is seasonalSeasonal
RH is seasonalSeasonal
NETRAD is seasonalSeasonal
PPFD_IN is seasonalSeasonal
CO2_F_MDS is seasonalSeasonal
TS_F_MDS_1 is seasonalSeasonal
LE_F_MDS is seasonalSeasonal
H_F_MDS is seasonalSeasonal
NPP_DT_VUT_USTAR50 is seasonalSeasonal
Year is seasonalSeasonal
Month is seasonalSeasonal
DoY is seasonalSeasonal
TIMESTAMP_START has unique valuesUnique
SW_IN_POT has 16891 (48.2%) zerosZeros
SW_IN_F_MDS has 441 (1.3%) zerosZeros
VPD_ERA has 1257 (3.6%) zerosZeros
P_F has 12548 (35.8%) zerosZeros
PPFD_IN has 355 (1.0%) zerosZeros

Reproduction

Analysis started2023-09-10 23:07:22.102756
Analysis finished2023-09-10 23:15:46.508667
Duration8 minutes and 24.41 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Distinct35040
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size273.9 KiB
Minimum2018-01-01 00:00:00
Maximum2019-12-31 23:30:00
2023-09-10T20:15:47.694492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:48.248937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

TA_F_MDS
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct10261
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.687193
Minimum16.646
Maximum35.454
Zeros0
Zeros (%)0.0%
Memory size273.9 KiB
2023-09-10T20:15:49.264227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum16.646
5-th percentile22.318
Q123.65
median24.968
Q327.517
95-th percentile30.88605
Maximum35.454
Range18.808
Interquartile range (IQR)3.867

Descriptive statistics

Standard deviation2.7061071
Coefficient of variation (CV)0.1053485
Kurtosis-0.20986186
Mean25.687193
Median Absolute Deviation (MAD)1.663
Skewness0.67347997
Sum900079.23
Variance7.3230157
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value8.626641247 Ă— 10-28
2023-09-10T20:15:50.681455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.087 18
 
0.1%
24.397 15
 
< 0.1%
23.652 15
 
< 0.1%
24.278 15
 
< 0.1%
24.505 15
 
< 0.1%
23.607 15
 
< 0.1%
23.895 15
 
< 0.1%
24.748 15
 
< 0.1%
23.76 14
 
< 0.1%
23.247 14
 
< 0.1%
Other values (10251) 34889
99.6%
ValueCountFrequency (%)
16.646 1
< 0.1%
17.158 1
< 0.1%
17.296 1
< 0.1%
17.559 1
< 0.1%
17.794 1
< 0.1%
17.873 1
< 0.1%
18.04 1
< 0.1%
18.074 1
< 0.1%
18.08 1
< 0.1%
18.124 1
< 0.1%
ValueCountFrequency (%)
35.454 1
< 0.1%
35.437 1
< 0.1%
35.414 1
< 0.1%
35.291 1
< 0.1%
35.158 1
< 0.1%
35.104 1
< 0.1%
35.042 1
< 0.1%
34.963 1
< 0.1%
34.918 1
< 0.1%
34.714 1
< 0.1%
2023-09-10T20:15:52.556820image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ACF and PACF

SW_IN_POT
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  ZEROS 

Distinct9280
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean419.29992
Minimum0
Maximum1396.66
Zeros16891
Zeros (%)48.2%
Memory size273.9 KiB
2023-09-10T20:15:53.478141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median21.75625
Q3926.552
95-th percentile1320.32
Maximum1396.66
Range1396.66
Interquartile range (IQR)926.552

Descriptive statistics

Standard deviation508.63337
Coefficient of variation (CV)1.2130538
Kurtosis-1.2177985
Mean419.29992
Median Absolute Deviation (MAD)21.75625
Skewness0.67732575
Sum14692269
Variance258707.91
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.9097289582
2023-09-10T20:15:54.040889image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16891
48.2%
1324 6
 
< 0.1%
1145.47 6
 
< 0.1%
1075.6 6
 
< 0.1%
1282.16 6
 
< 0.1%
1034.67 6
 
< 0.1%
1241.87 6
 
< 0.1%
1371.77 6
 
< 0.1%
1297.14 6
 
< 0.1%
1299.28 6
 
< 0.1%
Other values (9270) 18095
51.6%
ValueCountFrequency (%)
0 16891
48.2%
0.00355701 2
 
< 0.1%
0.0248898 2
 
< 0.1%
0.0348239 2
 
< 0.1%
0.0558635 2
 
< 0.1%
0.0769754 2
 
< 0.1%
0.0831225 2
 
< 0.1%
0.0876438 2
 
< 0.1%
0.0925519 2
 
< 0.1%
0.0935894 1
 
< 0.1%
ValueCountFrequency (%)
1396.66 2
< 0.1%
1396.54 2
< 0.1%
1396.47 2
< 0.1%
1396.45 2
< 0.1%
1396.37 4
< 0.1%
1396.22 2
< 0.1%
1396.13 2
< 0.1%
1396 2
< 0.1%
1395.83 2
< 0.1%
1395.47 2
< 0.1%
2023-09-10T20:15:55.187545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ACF and PACF

SW_IN_F_MDS
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  ZEROS 

Distinct10445
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean160.97425
Minimum0
Maximum1083
Zeros441
Zeros (%)1.3%
Memory size273.9 KiB
2023-09-10T20:15:55.785348image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.013
Q10.071
median4.071
Q3282.525
95-th percentile687.505
Maximum1083
Range1083
Interquartile range (IQR)282.454

Descriptive statistics

Standard deviation236.10583
Coefficient of variation (CV)1.4667304
Kurtosis0.78306831
Mean160.97425
Median Absolute Deviation (MAD)4.069
Skewness1.3857163
Sum5640537.8
Variance55745.961
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value7.525549818 Ă— 10-29
2023-09-10T20:15:56.478295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 441
 
1.3%
0.015 391
 
1.1%
0.014 386
 
1.1%
0.013 363
 
1.0%
0.016 339
 
1.0%
0.017 328
 
0.9%
0.012 308
 
0.9%
0.018 277
 
0.8%
0.011 236
 
0.7%
0.019 233
 
0.7%
Other values (10435) 31738
90.6%
ValueCountFrequency (%)
0 441
1.3%
0.001 11
 
< 0.1%
0.002 21
 
0.1%
0.003 33
 
0.1%
0.004 26
 
0.1%
0.005 23
 
0.1%
0.006 50
 
0.1%
0.007 40
 
0.1%
0.008 72
 
0.2%
0.009 132
 
0.4%
ValueCountFrequency (%)
1083 1
< 0.1%
1053 1
< 0.1%
1052 1
< 0.1%
1024 1
< 0.1%
1016 1
< 0.1%
1012 1
< 0.1%
1007 1
< 0.1%
1001 2
< 0.1%
1000 1
< 0.1%
998 1
< 0.1%
2023-09-10T20:15:58.549679image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ACF and PACF

LW_IN_F
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct14193
Distinct (%)40.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean418.08458
Minimum334.149
Maximum453.598
Zeros0
Zeros (%)0.0%
Memory size273.9 KiB
2023-09-10T20:16:00.402356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum334.149
5-th percentile398.77575
Q1411.0375
median418.949
Q3426.38925
95-th percentile435.2381
Maximum453.598
Range119.449
Interquartile range (IQR)15.35175

Descriptive statistics

Standard deviation11.965881
Coefficient of variation (CV)0.028620718
Kurtosis3.1291055
Mean418.08458
Median Absolute Deviation (MAD)7.671
Skewness-0.92271818
Sum14649684
Variance143.18231
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.925372736 Ă— 10-19
2023-09-10T20:16:00.917250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
424.085 14
 
< 0.1%
405.603 10
 
< 0.1%
424.858 10
 
< 0.1%
415.09 10
 
< 0.1%
407.896 10
 
< 0.1%
415.965 8
 
< 0.1%
426.665 8
 
< 0.1%
425.794 8
 
< 0.1%
406.742 8
 
< 0.1%
427.411 8
 
< 0.1%
Other values (14183) 34946
99.7%
ValueCountFrequency (%)
334.149 2
< 0.1%
337.228 2
< 0.1%
339.054 2
< 0.1%
339.159 2
< 0.1%
342.868 2
< 0.1%
343.62 2
< 0.1%
343.847 2
< 0.1%
344.435 2
< 0.1%
345.336 2
< 0.1%
345.429 2
< 0.1%
ValueCountFrequency (%)
453.598 2
< 0.1%
452.891 2
< 0.1%
452.227 2
< 0.1%
450.738 2
< 0.1%
450.17 2
< 0.1%
450.167 2
< 0.1%
449.965 2
< 0.1%
449.692 2
< 0.1%
449.661 2
< 0.1%
449.656 2
< 0.1%
2023-09-10T20:16:02.922498image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ACF and PACF

LW_IN_JSB_F
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct23070
Distinct (%)65.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean482.93272
Minimum364.068
Maximum554.475
Zeros0
Zeros (%)0.0%
Memory size273.9 KiB
2023-09-10T20:16:04.436133image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum364.068
5-th percentile446.8559
Q1471.564
median482.6075
Q3496.1555
95-th percentile519.8324
Maximum554.475
Range190.407
Interquartile range (IQR)24.5915

Descriptive statistics

Standard deviation22.863622
Coefficient of variation (CV)0.047343286
Kurtosis2.5536884
Mean482.93272
Median Absolute Deviation (MAD)12.144
Skewness-0.72912049
Sum16921963
Variance522.74521
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.709105382 Ă— 10-22
2023-09-10T20:16:04.970970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
470.636 13
 
< 0.1%
480.676 12
 
< 0.1%
471.799 12
 
< 0.1%
475.076 12
 
< 0.1%
472.075 12
 
< 0.1%
472.32 12
 
< 0.1%
480.842 12
 
< 0.1%
473.896 11
 
< 0.1%
473.047 11
 
< 0.1%
476.989 11
 
< 0.1%
Other values (23060) 34922
99.7%
ValueCountFrequency (%)
364.068 1
< 0.1%
366.439 1
< 0.1%
367.694 1
< 0.1%
368.266 1
< 0.1%
369.291 1
< 0.1%
369.372 1
< 0.1%
370.383 1
< 0.1%
370.448 1
< 0.1%
371.518 1
< 0.1%
371.543 1
< 0.1%
ValueCountFrequency (%)
554.475 1
< 0.1%
553.582 1
< 0.1%
553.55 1
< 0.1%
553.117 1
< 0.1%
551.867 1
< 0.1%
551.399 1
< 0.1%
551.106 1
< 0.1%
550.488 1
< 0.1%
549.997 1
< 0.1%
549.41 1
< 0.1%
2023-09-10T20:16:16.097657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ACF and PACF

VPD_ERA
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  ZEROS 

Distinct11022
Distinct (%)31.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7364701
Minimum0
Maximum24.373
Zeros1257
Zeros (%)3.6%
Memory size273.9 KiB
2023-09-10T20:16:17.170162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.058
Q10.713
median2.031
Q35.377
95-th percentile12.751
Maximum24.373
Range24.373
Interquartile range (IQR)4.664

Descriptive statistics

Standard deviation4.1587233
Coefficient of variation (CV)1.1130086
Kurtosis1.9097117
Mean3.7364701
Median Absolute Deviation (MAD)1.645
Skewness1.5339199
Sum130925.91
Variance17.29498
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value6.68411299 Ă— 10-29
2023-09-10T20:16:17.580107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1257
 
3.6%
0.788 24
 
0.1%
0.573 23
 
0.1%
0.529 22
 
0.1%
0.635 20
 
0.1%
0.503 20
 
0.1%
0.518 19
 
0.1%
0.01 19
 
0.1%
0.22 19
 
0.1%
0.231 19
 
0.1%
Other values (11012) 33598
95.9%
ValueCountFrequency (%)
0 1257
3.6%
0.001 6
 
< 0.1%
0.002 7
 
< 0.1%
0.003 7
 
< 0.1%
0.004 10
 
< 0.1%
0.005 5
 
< 0.1%
0.006 5
 
< 0.1%
0.007 9
 
< 0.1%
0.008 12
 
< 0.1%
0.009 5
 
< 0.1%
ValueCountFrequency (%)
24.373 1
< 0.1%
24.043 1
< 0.1%
23.722 1
< 0.1%
23.713 1
< 0.1%
23.688 1
< 0.1%
23.012 1
< 0.1%
23.003 1
< 0.1%
22.975 1
< 0.1%
22.939 1
< 0.1%
22.909 1
< 0.1%
2023-09-10T20:16:18.473611image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ACF and PACF

PA_ERA
Numeric time series

NON STATIONARY  SEASONAL 

Distinct1509
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.82976
Minimum98.009
Maximum99.74
Zeros0
Zeros (%)0.0%
Memory size273.9 KiB
2023-09-10T20:16:19.129739image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum98.009
5-th percentile98.402
Q198.655
median98.83
Q399.004
95-th percentile99.254
Maximum99.74
Range1.731
Interquartile range (IQR)0.349

Descriptive statistics

Standard deviation0.25881123
Coefficient of variation (CV)0.002618758
Kurtosis-0.12154421
Mean98.82976
Median Absolute Deviation (MAD)0.175
Skewness0.04961456
Sum3462994.8
Variance0.066983251
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.736917737 Ă— 10-18
2023-09-10T20:16:19.590361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98.892 72
 
0.2%
98.867 71
 
0.2%
98.757 70
 
0.2%
98.746 70
 
0.2%
98.727 69
 
0.2%
98.852 69
 
0.2%
98.847 68
 
0.2%
98.762 68
 
0.2%
98.788 67
 
0.2%
98.789 67
 
0.2%
Other values (1499) 34349
98.0%
ValueCountFrequency (%)
98.009 1
< 0.1%
98.022 1
< 0.1%
98.035 1
< 0.1%
98.047 1
< 0.1%
98.053 1
< 0.1%
98.055 1
< 0.1%
98.064 1
< 0.1%
98.066 2
< 0.1%
98.07 1
< 0.1%
98.071 1
< 0.1%
ValueCountFrequency (%)
99.74 1
 
< 0.1%
99.728 1
 
< 0.1%
99.723 3
< 0.1%
99.716 2
< 0.1%
99.709 1
 
< 0.1%
99.707 1
 
< 0.1%
99.706 2
< 0.1%
99.697 1
 
< 0.1%
99.689 2
< 0.1%
99.688 1
 
< 0.1%
2023-09-10T20:16:20.425309image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ACF and PACF

P_F
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  ZEROS 

Distinct1498
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1617718
Minimum0
Maximum4.809
Zeros12548
Zeros (%)35.8%
Memory size273.9 KiB
2023-09-10T20:16:21.302691image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.01
Q30.115
95-th percentile0.9012
Maximum4.809
Range4.809
Interquartile range (IQR)0.115

Descriptive statistics

Standard deviation0.39751466
Coefficient of variation (CV)2.4572555
Kurtosis27.282971
Mean0.1617718
Median Absolute Deviation (MAD)0.01
Skewness4.5353091
Sum5668.484
Variance0.15801791
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2023-09-10T20:16:21.684683image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12548
35.8%
0.001 1466
 
4.2%
0.002 650
 
1.9%
0.003 536
 
1.5%
0.004 452
 
1.3%
0.005 408
 
1.2%
0.006 356
 
1.0%
0.007 324
 
0.9%
0.008 306
 
0.9%
0.009 288
 
0.8%
Other values (1488) 17706
50.5%
ValueCountFrequency (%)
0 12548
35.8%
0.001 1466
 
4.2%
0.002 650
 
1.9%
0.003 536
 
1.5%
0.004 452
 
1.3%
0.005 408
 
1.2%
0.006 356
 
1.0%
0.007 324
 
0.9%
0.008 306
 
0.9%
0.009 288
 
0.8%
ValueCountFrequency (%)
4.809 2
< 0.1%
4.699 2
< 0.1%
4.6 2
< 0.1%
4.46 2
< 0.1%
4.457 2
< 0.1%
4.413 2
< 0.1%
4.324 2
< 0.1%
4.182 2
< 0.1%
4.092 2
< 0.1%
4.051 2
< 0.1%
2023-09-10T20:16:22.617810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ACF and PACF

WS_F
Numeric time series

NON STATIONARY  SEASONAL 

Distinct3072
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2076588
Minimum0.008
Maximum5.673
Zeros0
Zeros (%)0.0%
Memory size273.9 KiB
2023-09-10T20:16:23.381411image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.008
5-th percentile0.36595
Q10.76275
median1.14
Q31.568
95-th percentile2.26605
Maximum5.673
Range5.665
Interquartile range (IQR)0.80525

Descriptive statistics

Standard deviation0.60249732
Coefficient of variation (CV)0.49889699
Kurtosis1.4436392
Mean1.2076588
Median Absolute Deviation (MAD)0.4
Skewness0.84422912
Sum42316.363
Variance0.36300302
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2023-09-10T20:16:23.830944image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.115 38
 
0.1%
0.803 38
 
0.1%
0.833 37
 
0.1%
0.92 37
 
0.1%
0.805 36
 
0.1%
1.344 35
 
0.1%
1.016 35
 
0.1%
0.926 35
 
0.1%
0.913 35
 
0.1%
0.978 34
 
0.1%
Other values (3062) 34680
99.0%
ValueCountFrequency (%)
0.008 1
< 0.1%
0.019 2
< 0.1%
0.02 1
< 0.1%
0.021 1
< 0.1%
0.022 1
< 0.1%
0.023 2
< 0.1%
0.024 1
< 0.1%
0.025 1
< 0.1%
0.028 1
< 0.1%
0.029 1
< 0.1%
ValueCountFrequency (%)
5.673 1
< 0.1%
5.546 1
< 0.1%
4.907 1
< 0.1%
4.843 1
< 0.1%
4.842 1
< 0.1%
4.757 2
< 0.1%
4.73 1
< 0.1%
4.695 1
< 0.1%
4.685 1
< 0.1%
4.634 1
< 0.1%
2023-09-10T20:16:24.929568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ACF and PACF

WD
Real number (ℝ)

Distinct26446
Distinct (%)> 99.9%
Missing8593
Missing (%)24.5%
Infinite0
Infinite (%)0.0%
Mean181.73394
Minimum0.008075
Maximum359.98886
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size273.9 KiB
2023-09-10T20:16:26.841086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.008075
5-th percentile10.96195
Q173.992747
median183.99887
Q3287.48155
95-th percentile349.09364
Maximum359.98886
Range359.98078
Interquartile range (IQR)213.48881

Descriptive statistics

Standard deviation113.44844
Coefficient of variation (CV)0.62425566
Kurtosis-1.3580073
Mean181.73394
Median Absolute Deviation (MAD)106.17482
Skewness-0.03330385
Sum4806317.4
Variance12870.548
MonotonicityNot monotonic
2023-09-10T20:16:27.318207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
258.731408 2
 
< 0.1%
22.20081 1
 
< 0.1%
23.17391 1
 
< 0.1%
121.964815 1
 
< 0.1%
115.296494 1
 
< 0.1%
116.051932 1
 
< 0.1%
80.333305 1
 
< 0.1%
64.344576 1
 
< 0.1%
112.225019 1
 
< 0.1%
130.939517 1
 
< 0.1%
Other values (26436) 26436
75.4%
(Missing) 8593
 
24.5%
ValueCountFrequency (%)
0.008075 1
< 0.1%
0.021357 1
< 0.1%
0.021952 1
< 0.1%
0.02582 1
< 0.1%
0.036169 1
< 0.1%
0.036819 1
< 0.1%
0.050894 1
< 0.1%
0.054755 1
< 0.1%
0.063642 1
< 0.1%
0.068249 1
< 0.1%
ValueCountFrequency (%)
359.988858 1
< 0.1%
359.982808 1
< 0.1%
359.980591 1
< 0.1%
359.974032 1
< 0.1%
359.973113 1
< 0.1%
359.969801 1
< 0.1%
359.959045 1
< 0.1%
359.952244 1
< 0.1%
359.946123 1
< 0.1%
359.916635 1
< 0.1%

USTAR
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25434
Distinct (%)96.2%
Missing8593
Missing (%)24.5%
Infinite0
Infinite (%)0.0%
Mean0.18715402
Minimum0.004563
Maximum1.279706
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size273.9 KiB
2023-09-10T20:16:28.152939image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.004563
5-th percentile0.0477663
Q10.0943755
median0.153543
Q30.248674
95-th percentile0.4346851
Maximum1.279706
Range1.275143
Interquartile range (IQR)0.1542985

Descriptive statistics

Standard deviation0.12554928
Coefficient of variation (CV)0.67083403
Kurtosis2.7086837
Mean0.18715402
Median Absolute Deviation (MAD)0.06968
Skewness1.4165681
Sum4949.6623
Variance0.015762622
MonotonicityNot monotonic
2023-09-10T20:16:28.629634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.098258 4
 
< 0.1%
0.15301 3
 
< 0.1%
0.126712 3
 
< 0.1%
0.146817 3
 
< 0.1%
0.102988 3
 
< 0.1%
0.121178 3
 
< 0.1%
0.211469 3
 
< 0.1%
0.140049 3
 
< 0.1%
0.107363 3
 
< 0.1%
0.068046 3
 
< 0.1%
Other values (25424) 26416
75.4%
(Missing) 8593
 
24.5%
ValueCountFrequency (%)
0.004563 1
< 0.1%
0.008137 1
< 0.1%
0.008502 1
< 0.1%
0.008564 1
< 0.1%
0.008937 1
< 0.1%
0.009629 1
< 0.1%
0.009768 1
< 0.1%
0.010033 1
< 0.1%
0.010819 1
< 0.1%
0.011025 1
< 0.1%
ValueCountFrequency (%)
1.279706 1
< 0.1%
1.113754 1
< 0.1%
1.088441 1
< 0.1%
1.009235 1
< 0.1%
0.953634 1
< 0.1%
0.934259 1
< 0.1%
0.923364 1
< 0.1%
0.913306 1
< 0.1%
0.906874 1
< 0.1%
0.905238 1
< 0.1%

RH
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL 

Distinct16106
Distinct (%)47.4%
Missing1047
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean90.542143
Minimum36.811
Maximum100
Zeros0
Zeros (%)0.0%
Memory size273.9 KiB
2023-09-10T20:16:29.233553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum36.811
5-th percentile66.3226
Q183.427
median96.189
Q3100
95-th percentile100
Maximum100
Range63.189
Interquartile range (IQR)16.573

Descriptive statistics

Standard deviation11.79846
Coefficient of variation (CV)0.13030904
Kurtosis0.67773745
Mean90.542143
Median Absolute Deviation (MAD)3.811
Skewness-1.2249859
Sum3077799.1
Variance139.20365
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value9.241554615 Ă— 10-26
2023-09-10T20:16:29.910643image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 11997
34.2%
96.481 7
 
< 0.1%
99.106 7
 
< 0.1%
98.418 7
 
< 0.1%
99.003 7
 
< 0.1%
99.727 7
 
< 0.1%
99.861 6
 
< 0.1%
98.758 6
 
< 0.1%
99.666 6
 
< 0.1%
97.266 6
 
< 0.1%
Other values (16096) 21937
62.6%
(Missing) 1047
 
3.0%
ValueCountFrequency (%)
36.811 1
< 0.1%
36.885 1
< 0.1%
37.022 1
< 0.1%
38.171 1
< 0.1%
38.223 1
< 0.1%
38.328 1
< 0.1%
38.442 1
< 0.1%
39.541 1
< 0.1%
39.792 1
< 0.1%
39.972 1
< 0.1%
ValueCountFrequency (%)
100 11997
34.2%
99.999 2
 
< 0.1%
99.998 2
 
< 0.1%
99.997 2
 
< 0.1%
99.996 2
 
< 0.1%
99.995 3
 
< 0.1%
99.994 1
 
< 0.1%
99.993 2
 
< 0.1%
99.992 5
 
< 0.1%
99.991 1
 
< 0.1%
2023-09-10T20:16:30.995224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ACF and PACF

NETRAD
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL 

Distinct29151
Distinct (%)85.8%
Missing1047
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean111.22197
Minimum-152.4
Maximum909.27
Zeros0
Zeros (%)0.0%
Memory size273.9 KiB
2023-09-10T20:16:31.728644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-152.4
5-th percentile-35.9394
Q1-22.615
median-8.8212
Q3203.38
95-th percentile570.196
Maximum909.27
Range1061.67
Interquartile range (IQR)225.995

Descriptive statistics

Standard deviation203.94268
Coefficient of variation (CV)1.8336546
Kurtosis0.9950937
Mean111.22197
Median Absolute Deviation (MAD)24.8158
Skewness1.458291
Sum3780768.5
Variance41592.618
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value4.918101231 Ă— 10-30
2023-09-10T20:16:32.399412image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-19.033 6
 
< 0.1%
-10.964 5
 
< 0.1%
-22.832 5
 
< 0.1%
-14.07 5
 
< 0.1%
-16.722 5
 
< 0.1%
-19.09 5
 
< 0.1%
-17.621 5
 
< 0.1%
-23.687 5
 
< 0.1%
-18.917 5
 
< 0.1%
-18.059 5
 
< 0.1%
Other values (29141) 33942
96.9%
(Missing) 1047
 
3.0%
ValueCountFrequency (%)
-152.4 1
< 0.1%
-135.96 1
< 0.1%
-120.49 1
< 0.1%
-117.79 1
< 0.1%
-115.43 1
< 0.1%
-91.103 1
< 0.1%
-90.426 1
< 0.1%
-89.923 1
< 0.1%
-89.605 1
< 0.1%
-88.918 1
< 0.1%
ValueCountFrequency (%)
909.27 1
< 0.1%
897.28 1
< 0.1%
872.21 1
< 0.1%
869.89 1
< 0.1%
869.53 1
< 0.1%
859.75 1
< 0.1%
859.23 1
< 0.1%
850.16 1
< 0.1%
848.5 1
< 0.1%
847.78 1
< 0.1%
2023-09-10T20:16:33.446713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ACF and PACF

PPFD_IN
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL  ZEROS 

Distinct9362
Distinct (%)27.5%
Missing1047
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean330.4119
Minimum0
Maximum2194
Zeros355
Zeros (%)1.0%
Memory size273.9 KiB
2023-09-10T20:16:34.417628image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.004
Q10.031
median8.55
Q3582.5
95-th percentile1396
Maximum2194
Range2194
Interquartile range (IQR)582.469

Descriptive statistics

Standard deviation482.39643
Coefficient of variation (CV)1.459985
Kurtosis0.70902161
Mean330.4119
Median Absolute Deviation (MAD)8.549
Skewness1.366303
Sum11231692
Variance232706.32
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.004399586 Ă— 10-29
2023-09-10T20:16:34.893518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.003 523
 
1.5%
0.004 518
 
1.5%
0.005 440
 
1.3%
0.007 431
 
1.2%
0.008 413
 
1.2%
0.01 372
 
1.1%
0.006 365
 
1.0%
0.009 361
 
1.0%
0 355
 
1.0%
0.011 343
 
1.0%
Other values (9352) 29872
85.3%
(Missing) 1047
 
3.0%
ValueCountFrequency (%)
0 355
1.0%
0.001 342
1.0%
0.002 329
0.9%
0.003 523
1.5%
0.004 518
1.5%
0.005 440
1.3%
0.006 365
1.0%
0.007 431
1.2%
0.008 413
1.2%
0.009 361
1.0%
ValueCountFrequency (%)
2194 1
< 0.1%
2127 1
< 0.1%
2097 1
< 0.1%
2072 1
< 0.1%
2050 1
< 0.1%
2041 1
< 0.1%
2040 1
< 0.1%
2018 1
< 0.1%
2013 1
< 0.1%
2012 1
< 0.1%
2023-09-10T20:16:35.803899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ACF and PACF

CO2_F_MDS
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct28581
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean434.23104
Minimum321.683
Maximum838.597
Zeros0
Zeros (%)0.0%
Memory size273.9 KiB
2023-09-10T20:16:37.093085image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum321.683
5-th percentile384.54885
Q1413.60125
median429.09
Q3453.9675
95-th percentile492.60335
Maximum838.597
Range516.914
Interquartile range (IQR)40.36625

Descriptive statistics

Standard deviation33.190443
Coefficient of variation (CV)0.076434984
Kurtosis4.5605365
Mean434.23104
Median Absolute Deviation (MAD)19.3645
Skewness1.0724992
Sum15215456
Variance1101.6055
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value9.285986853 Ă— 10-15
2023-09-10T20:16:38.038652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
426.082 163
 
0.5%
421.601 75
 
0.2%
417.119 67
 
0.2%
418.198 58
 
0.2%
425.222 39
 
0.1%
424.864 30
 
0.1%
421.129 21
 
0.1%
420.403 18
 
0.1%
411.726 17
 
< 0.1%
457.838 16
 
< 0.1%
Other values (28571) 34536
98.6%
ValueCountFrequency (%)
321.683 1
< 0.1%
346.255 1
< 0.1%
357.237 1
< 0.1%
364.323 1
< 0.1%
365.376 1
< 0.1%
365.622 1
< 0.1%
365.98 1
< 0.1%
366.096 1
< 0.1%
366.162 1
< 0.1%
366.339 1
< 0.1%
ValueCountFrequency (%)
838.597 1
< 0.1%
811.655 1
< 0.1%
773.951 1
< 0.1%
758.623 1
< 0.1%
754.613 1
< 0.1%
744.193 1
< 0.1%
741.43 1
< 0.1%
725.891 1
< 0.1%
704.145 1
< 0.1%
702.647 1
< 0.1%
2023-09-10T20:16:39.030970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ACF and PACF

TS_F_MDS_1
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct4529
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.218508
Minimum20.19
Maximum28.167
Zeros0
Zeros (%)0.0%
Memory size273.9 KiB
2023-09-10T20:16:39.892490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum20.19
5-th percentile23.961
Q124.589
median25.094
Q325.836
95-th percentile26.83705
Maximum28.167
Range7.977
Interquartile range (IQR)1.247

Descriptive statistics

Standard deviation0.90980338
Coefficient of variation (CV)0.036076812
Kurtosis0.44086817
Mean25.218508
Median Absolute Deviation (MAD)0.5975
Skewness0.14770953
Sum883656.51
Variance0.82774218
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value4.014671542 Ă— 10-18
2023-09-10T20:16:40.406417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.863 29
 
0.1%
24.759 28
 
0.1%
24.829 28
 
0.1%
24.767 28
 
0.1%
24.822 28
 
0.1%
24.8 27
 
0.1%
24.764 27
 
0.1%
24.706 27
 
0.1%
24.824 26
 
0.1%
24.558 26
 
0.1%
Other values (4519) 34766
99.2%
ValueCountFrequency (%)
20.19 1
< 0.1%
20.231 1
< 0.1%
20.271 1
< 0.1%
20.295 1
< 0.1%
20.401 1
< 0.1%
20.468 1
< 0.1%
20.495 1
< 0.1%
20.535 1
< 0.1%
20.718 1
< 0.1%
20.732 1
< 0.1%
ValueCountFrequency (%)
28.167 1
 
< 0.1%
28.132 1
 
< 0.1%
28.121 1
 
< 0.1%
28.116 3
< 0.1%
28.097 1
 
< 0.1%
28.075 1
 
< 0.1%
28.072 1
 
< 0.1%
28.049 1
 
< 0.1%
28.028 1
 
< 0.1%
28.025 1
 
< 0.1%
2023-09-10T20:16:41.362775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ACF and PACF

LE_F_MDS
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct30957
Distinct (%)88.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.637565
Minimum-890.726
Maximum830.368
Zeros0
Zeros (%)0.0%
Memory size273.9 KiB
2023-09-10T20:16:42.009432image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-890.726
5-th percentile-2.6288525
Q13.3942525
median11.4519
Q3143.134
95-th percentile316.453
Maximum830.368
Range1721.094
Interquartile range (IQR)139.73975

Descriptive statistics

Standard deviation113.04862
Coefficient of variation (CV)1.4195388
Kurtosis1.3842666
Mean79.637565
Median Absolute Deviation (MAD)12.930035
Skewness1.3516473
Sum2790500.3
Variance12779.99
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.312712337 Ă— 10-28
2023-09-10T20:16:42.348883image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.1706 46
 
0.1%
55.1871 40
 
0.1%
1.78686 34
 
0.1%
110.997 32
 
0.1%
2.95633 31
 
0.1%
0.997849 30
 
0.1%
59.0961 24
 
0.1%
3.37933 21
 
0.1%
0.935616 19
 
0.1%
95.9957 19
 
0.1%
Other values (30947) 34744
99.2%
ValueCountFrequency (%)
-890.726 1
< 0.1%
-494.091 1
< 0.1%
-449.292 1
< 0.1%
-372.329 1
< 0.1%
-348.285 1
< 0.1%
-342.982 1
< 0.1%
-338.116 1
< 0.1%
-319.788 1
< 0.1%
-293.665 1
< 0.1%
-268.709 1
< 0.1%
ValueCountFrequency (%)
830.368 1
< 0.1%
718.51 1
< 0.1%
658.975 1
< 0.1%
651.613 1
< 0.1%
648.057 1
< 0.1%
639.812 1
< 0.1%
638.122 1
< 0.1%
630.574 1
< 0.1%
625.612 1
< 0.1%
624.827 1
< 0.1%
2023-09-10T20:16:43.092867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ACF and PACF

H_F_MDS
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct31357
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.441388
Minimum-67.5852
Maximum233.211
Zeros0
Zeros (%)0.0%
Memory size273.9 KiB
2023-09-10T20:16:43.663397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-67.5852
5-th percentile-7.7171515
Q1-3.2009475
median-1.019835
Q321.7344
95-th percentile85.82377
Maximum233.211
Range300.7962
Interquartile range (IQR)24.935347

Descriptive statistics

Standard deviation31.409595
Coefficient of variation (CV)2.1749707
Kurtosis3.7858816
Mean14.441388
Median Absolute Deviation (MAD)3.643925
Skewness1.9649931
Sum506026.23
Variance986.56266
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.367832088 Ă— 10-29
2023-09-10T20:16:44.073673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.10108 34
 
0.1%
2.02778 34
 
0.1%
-12.1429 32
 
0.1%
-3.91578 31
 
0.1%
-1.95131 27
 
0.1%
-7.68574 24
 
0.1%
-5.96818 22
 
0.1%
-3.88611 21
 
0.1%
98.1487 21
 
0.1%
14.4101 19
 
0.1%
Other values (31347) 34775
99.2%
ValueCountFrequency (%)
-67.5852 1
< 0.1%
-62.0882 1
< 0.1%
-59.145 1
< 0.1%
-58.2684 1
< 0.1%
-57.1396 1
< 0.1%
-53.2815 1
< 0.1%
-53.2095 1
< 0.1%
-52.1135 1
< 0.1%
-50.7903 1
< 0.1%
-49.3939 1
< 0.1%
ValueCountFrequency (%)
233.211 1
< 0.1%
206.889 2
< 0.1%
204.781 1
< 0.1%
203.388 1
< 0.1%
198.956 2
< 0.1%
195.864 1
< 0.1%
194.524 1
< 0.1%
194.379 1
< 0.1%
189.244 1
< 0.1%
185.809 1
< 0.1%
2023-09-10T20:16:44.772489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ACF and PACF

NPP_DT_VUT_USTAR50
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct35011
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1044854
Minimum-17.787439
Maximum43.3811
Zeros0
Zeros (%)0.0%
Memory size273.9 KiB
2023-09-10T20:16:45.457760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-17.787439
5-th percentile-10.967626
Q1-7.3618497
median-4.0255507
Q310.144333
95-th percentile22.314078
Maximum43.3811
Range61.168539
Interquartile range (IQR)17.506182

Descriptive statistics

Standard deviation11.144577
Coefficient of variation (CV)10.090288
Kurtosis-0.48193982
Mean1.1044854
Median Absolute Deviation (MAD)5.176765
Skewness0.83380741
Sum38701.17
Variance124.20159
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.169291115 Ă— 10-21
2023-09-10T20:16:45.924725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.9651 2
 
< 0.1%
10.2854 2
 
< 0.1%
11.3548 2
 
< 0.1%
6.2235 2
 
< 0.1%
13.40734 2
 
< 0.1%
9.52326 2
 
< 0.1%
8.80838 2
 
< 0.1%
19.3401 2
 
< 0.1%
4.3541 2
 
< 0.1%
18.0235 2
 
< 0.1%
Other values (35001) 35020
99.9%
ValueCountFrequency (%)
-17.78743946 1
< 0.1%
-17.71732604 1
< 0.1%
-17.6738126 1
< 0.1%
-17.6630456 1
< 0.1%
-17.66165287 1
< 0.1%
-17.6389425 1
< 0.1%
-17.60360958 1
< 0.1%
-17.6031858 1
< 0.1%
-17.58123946 1
< 0.1%
-17.5498918 1
< 0.1%
ValueCountFrequency (%)
43.3811 1
< 0.1%
42.31965 1
< 0.1%
40.04686 1
< 0.1%
39.84699 1
< 0.1%
39.76968 1
< 0.1%
39.62324 1
< 0.1%
39.28922 1
< 0.1%
38.86339 1
< 0.1%
38.2167 1
< 0.1%
38.09477 1
< 0.1%
2023-09-10T20:16:46.956165image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ACF and PACF

Year
Numeric time series

NON STATIONARY  SEASONAL 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.5
Minimum2018
Maximum2019
Zeros0
Zeros (%)0.0%
Memory size273.9 KiB
2023-09-10T20:16:47.583741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2018
5-th percentile2018
Q12018
median2018.5
Q32019
95-th percentile2019
Maximum2019
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.50000713
Coefficient of variation (CV)0.00024771223
Kurtosis-2.0001142
Mean2018.5
Median Absolute Deviation (MAD)0.5
Skewness0
Sum70728240
Variance0.25000713
MonotonicityIncreasing
Augmented Dickey-Fuller test p-value0.7532747104
2023-09-10T20:16:47.839332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
2018 17520
50.0%
2019 17520
50.0%
ValueCountFrequency (%)
2018 17520
50.0%
2019 17520
50.0%
ValueCountFrequency (%)
2019 17520
50.0%
2018 17520
50.0%
2023-09-10T20:16:48.649298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ACF and PACF

Month
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5260274
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size273.9 KiB
2023-09-10T20:16:49.321476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4479005
Coefficient of variation (CV)0.52833068
Kurtosis-1.207053
Mean6.5260274
Median Absolute Deviation (MAD)3
Skewness-0.010456852
Sum228672
Variance11.888018
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.4100461101
2023-09-10T20:16:49.587420image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 2976
8.5%
3 2976
8.5%
5 2976
8.5%
7 2976
8.5%
8 2976
8.5%
10 2976
8.5%
12 2976
8.5%
4 2880
8.2%
6 2880
8.2%
9 2880
8.2%
Other values (2) 5568
15.9%
ValueCountFrequency (%)
1 2976
8.5%
2 2688
7.7%
3 2976
8.5%
4 2880
8.2%
5 2976
8.5%
6 2880
8.2%
7 2976
8.5%
8 2976
8.5%
9 2880
8.2%
10 2976
8.5%
ValueCountFrequency (%)
12 2976
8.5%
11 2880
8.2%
10 2976
8.5%
9 2880
8.2%
8 2976
8.5%
7 2976
8.5%
6 2880
8.2%
5 2976
8.5%
4 2880
8.2%
3 2976
8.5%
2023-09-10T20:16:50.442530image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ACF and PACF

DoY
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct365
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183
Minimum1
Maximum365
Zeros0
Zeros (%)0.0%
Memory size273.9 KiB
2023-09-10T20:16:51.110908image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19
Q192
median183
Q3274
95-th percentile347
Maximum365
Range364
Interquartile range (IQR)182

Descriptive statistics

Standard deviation105.36753
Coefficient of variation (CV)0.57577886
Kurtosis-1.200018
Mean183
Median Absolute Deviation (MAD)91
Skewness0
Sum6412320
Variance11102.317
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.4146540776
2023-09-10T20:16:51.540595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 96
 
0.3%
252 96
 
0.3%
250 96
 
0.3%
249 96
 
0.3%
248 96
 
0.3%
247 96
 
0.3%
246 96
 
0.3%
245 96
 
0.3%
244 96
 
0.3%
243 96
 
0.3%
Other values (355) 34080
97.3%
ValueCountFrequency (%)
1 96
0.3%
2 96
0.3%
3 96
0.3%
4 96
0.3%
5 96
0.3%
6 96
0.3%
7 96
0.3%
8 96
0.3%
9 96
0.3%
10 96
0.3%
ValueCountFrequency (%)
365 96
0.3%
364 96
0.3%
363 96
0.3%
362 96
0.3%
361 96
0.3%
360 96
0.3%
359 96
0.3%
358 96
0.3%
357 96
0.3%
356 96
0.3%
2023-09-10T20:16:52.387007image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ACF and PACF

Interactions

2023-09-10T20:15:31.759552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:12:37.926441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:12:52.364247image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:00.262417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:07.018437image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:14.178186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:21.042804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:29.436806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:37.831921image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:46.188657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:52.401400image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:59.384171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:07.272303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:14.235130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:21.416765image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:29.120119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:37.463490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:45.275795image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:53.158999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:00.242561image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:12.690309image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:21.736765image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:32.396665image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:12:38.832409image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:12:52.851942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:00.558443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:07.310625image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:14.487075image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:21.383392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:29.986400image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:38.164038image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:46.483190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:52.717640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:59.737731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:07.572820image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:14.528670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:21.738318image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:29.430287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:37.865624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:45.607163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:53.497439image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:00.581575image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:13.064508image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:22.281704image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:33.027463image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:12:39.588411image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:12:53.271131image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:00.834888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:07.596271image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:14.783257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:21.679154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:30.381418image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:38.482496image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:46.786422image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:53.018344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:00.107324image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:07.863382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:14.827839image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:22.060249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:29.812304image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:38.180900image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:45.987921image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:53.831909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:00.907750image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:13.387469image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:22.786297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:33.458467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:12:39.922550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:12:53.594583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:01.102511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:07.854445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:15.114187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:21.999278image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:30.828409image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:38.784670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:47.104601image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:53.308630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:00.413561image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:08.153391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:15.102578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:22.376425image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:30.153788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:38.486331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:46.306800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:54.139866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:01.241396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:13.670662image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:23.416966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:34.070590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:12:40.445764image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:12:53.944448image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:01.413441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:08.135125image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:15.447616image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:22.328854image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:31.199419image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:39.095263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:47.370080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:53.592305image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:00.729389image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:08.422019image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:15.407218image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-09-10T20:14:34.570298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:42.290941image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:49.876814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:57.351211image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:07.648348image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:17.663206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:27.442243image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:38.730245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:12:48.010725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:12:57.859094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:04.637182image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:11.553292image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:18.363960image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:25.992622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:34.963016image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:43.123373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:50.157856image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:56.659389image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:04.552883image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:11.529781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:18.580575image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:26.526392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:34.865040image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:42.577127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:50.288186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:57.649396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:08.768970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:17.996160image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:27.827579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:39.027406image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:12:48.714270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:12:58.156031image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:04.925413image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:11.858613image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:18.627198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:26.630337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:35.324099image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:43.521515image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:50.435626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:56.989472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:04.864570image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:12.043025image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:18.978858image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:26.912090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:35.258888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:42.879920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:50.586156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:57.917510image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:09.534824image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:18.556182image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:28.234547image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:39.485443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:12:49.135827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:12:58.450866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:05.215727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:12.201657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:18.969360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:27.177455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:35.780249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:44.047042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:50.718294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:57.417265image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:05.543377image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:12.549716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:19.437639image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:27.281172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:35.677764image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:43.333952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:50.959079image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:58.304155image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:10.304475image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:19.012709image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:28.577377image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:40.020448image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:12:49.559592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:12:58.758963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:05.506203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:12.491900image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:19.310734image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:27.611521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:36.112369image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:44.356218image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:51.018518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:57.755075image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:05.824697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:12.848121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:19.812726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:27.577382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:35.967809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:43.652288image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:51.302390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:58.583649image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:10.779827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:19.563429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:28.892913image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:40.394396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:12:50.046751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:12:59.061757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:05.789489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:12.755207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:19.620059image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:28.009384image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:36.442487image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:44.626955image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:51.284646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:58.036622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:06.131020image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:13.123922image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:20.151339image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:27.899223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:36.239999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:43.978035image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:51.638710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:58.879376image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:11.256093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:20.013287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:29.296907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:40.869619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:12:50.524714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:12:59.340380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:06.093287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:13.088804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:19.972960image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:28.363025image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:36.781346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:44.932335image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:51.595084image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:58.316486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:06.412827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:13.411017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:20.444056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:28.175763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:36.534500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:44.323925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:51.990561image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:59.264091image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:11.566248image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:20.646863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:30.285984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:41.361945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:12:51.169301image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:12:59.631543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:06.397597image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:13.602983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:20.293637image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:28.684645image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:37.069489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:45.502823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:51.861593image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:58.599383image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:06.695094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:13.687364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:20.727215image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:28.441629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:36.845278image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:44.623913image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:52.535032image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:59.614499image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:11.976112image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:20.961960image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:30.942233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:41.746166image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:12:51.788929image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:12:59.901695image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:06.705343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:13.891554image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:20.663483image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:28.999123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:37.439227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:45.855724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:52.123230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:13:59.020643image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:06.974295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:13.949976image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:21.052936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:28.769516image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:37.149905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:44.972539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:52.825330image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:14:59.924760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:12.315171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:21.290580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-09-10T20:15:31.360076image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-09-10T20:16:53.465558image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
TA_F_MDSSW_IN_POTSW_IN_F_MDSLW_IN_FLW_IN_JSB_FVPD_ERAPA_ERAP_FWS_FWDUSTARRHNETRADPPFD_INCO2_F_MDSTS_F_MDS_1LE_F_MDSH_F_MDSNPP_DT_VUT_USTAR50YearMonthDoY
TA_F_MDS1.0000.5520.5060.3950.6860.780-0.2940.2040.007-0.2040.333-0.8720.4740.548-0.5880.8250.5720.4840.4840.011-0.024-0.023
SW_IN_POT0.5521.0000.9100.5910.3030.6690.1400.4550.007-0.1880.550-0.6250.8680.911-0.4140.6250.8140.7350.8500.000-0.003-0.003
SW_IN_F_MDS0.5060.9101.0000.5340.1850.6030.1350.418-0.004-0.1620.527-0.5740.8720.873-0.4150.5710.7920.7240.8240.1400.0250.025
LW_IN_F0.3950.5910.5341.0000.3150.382-0.1280.5370.028-0.1180.373-0.3340.5430.520-0.2700.5660.4760.4280.4610.092-0.040-0.036
LW_IN_JSB_F0.6860.3030.1850.3151.0000.490-0.3200.1580.016-0.1290.147-0.4980.1780.231-0.3720.5950.2700.2010.216-0.012-0.066-0.067
VPD_ERA0.7800.6690.6030.3820.4901.000-0.1490.2190.072-0.1890.422-0.8170.5490.620-0.6100.8070.6380.5200.552-0.0140.0040.002
PA_ERA-0.2940.1400.135-0.128-0.320-0.1491.0000.076-0.0630.0560.0490.1160.1410.0850.182-0.3440.0740.1440.1860.036-0.034-0.034
P_F0.2040.4550.4180.5370.1580.2190.0761.0000.033-0.0700.320-0.1860.4500.383-0.1940.3610.3660.3400.3730.028-0.056-0.053
WS_F0.0070.007-0.0040.0280.0160.072-0.0630.0331.0000.1730.309-0.034-0.020-0.013-0.1180.0590.045-0.073-0.0090.045-0.000-0.000
WD-0.204-0.188-0.162-0.118-0.129-0.1890.056-0.0700.1731.000-0.0340.188-0.178-0.1800.082-0.197-0.192-0.148-0.1480.0450.0400.043
USTAR0.3330.5500.5270.3730.1470.4220.0490.3200.309-0.0341.000-0.3780.5400.521-0.3190.4260.5820.3670.5030.006-0.015-0.014
RH-0.872-0.625-0.574-0.334-0.498-0.8170.116-0.186-0.0340.188-0.3781.000-0.535-0.6220.637-0.710-0.654-0.541-0.5680.0910.0120.012
NETRAD0.4740.8680.8720.5430.1780.5490.1410.450-0.020-0.1780.540-0.5351.0000.845-0.3300.5430.7910.7240.8070.009-0.018-0.017
PPFD_IN0.5480.9110.8730.5200.2310.6200.0850.383-0.013-0.1800.521-0.6220.8451.000-0.3750.5850.7920.7150.811-0.0640.0950.096
CO2_F_MDS-0.588-0.414-0.415-0.270-0.372-0.6100.182-0.194-0.1180.082-0.3190.637-0.330-0.3751.000-0.554-0.458-0.318-0.343-0.145-0.178-0.180
TS_F_MDS_10.8250.6250.5710.5660.5950.807-0.3440.3610.059-0.1970.426-0.7100.5430.585-0.5541.0000.5860.4740.4900.049-0.002-0.001
LE_F_MDS0.5720.8140.7920.4760.2700.6380.0740.3660.045-0.1920.582-0.6540.7910.792-0.4580.5861.0000.6070.771-0.029-0.008-0.008
H_F_MDS0.4840.7350.7240.4280.2010.5200.1440.340-0.073-0.1480.367-0.5410.7240.715-0.3180.4740.6071.0000.705-0.003-0.036-0.034
NPP_DT_VUT_USTAR500.4840.8500.8240.4610.2160.5520.1860.373-0.009-0.1480.503-0.5680.8070.811-0.3430.4900.7710.7051.000-0.034-0.069-0.067
Year0.0110.0000.1400.092-0.012-0.0140.0360.0280.0450.0450.0060.0910.009-0.064-0.1450.049-0.029-0.003-0.0341.0000.0000.000
Month-0.024-0.0030.025-0.040-0.0660.004-0.034-0.056-0.0000.040-0.0150.012-0.0180.095-0.178-0.002-0.008-0.036-0.0690.0001.0000.997
DoY-0.023-0.0030.025-0.036-0.0670.002-0.034-0.053-0.0000.043-0.0140.012-0.0170.096-0.180-0.001-0.008-0.034-0.0670.0000.9971.000

Missing values

2023-09-10T20:15:43.735325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-10T20:15:44.823123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-10T20:15:46.003702image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

TIMESTAMP_STARTTA_F_MDSSW_IN_POTSW_IN_F_MDSLW_IN_FLW_IN_JSB_FVPD_ERAPA_ERAP_FWS_FWDUSTARRHNETRADPPFD_INCO2_F_MDSTS_F_MDS_1LE_F_MDSH_F_MDSNPP_DT_VUT_USTAR50YearMonthDoY
02018-01-01 00:00:0024.1960.00.000411.887479.3381.10598.8300.0481.091NaNNaNNaNNaNNaN443.97924.4747.21952-4.53360-9.381420201811
12018-01-01 00:30:0024.1950.00.000411.887479.3480.88998.7930.0480.952NaNNaNNaNNaNNaN443.97924.4107.21952-4.47688-9.381070201811
22018-01-01 01:00:0023.1800.00.082415.333471.4560.67298.7560.1041.459245.8492740.08440999.914-21.6950.014454.89924.3459.34216-2.80919-9.009657201811
32018-01-01 01:30:0022.7390.00.082415.333466.8730.63098.7250.1041.830272.0328120.25149499.768-19.8870.014451.78424.2816.729595.66948-8.856457201811
42018-01-01 02:00:0022.5370.00.085411.126464.9850.58898.6950.0681.438275.2975690.208431100.000-17.9550.013451.78424.2416.729594.18561-8.785909201811
52018-01-01 02:30:0022.5150.00.083411.126464.7620.45598.6840.0680.951309.5080080.148144100.000-15.9020.014451.78424.2026.729592.95287-8.778708201811
62018-01-01 03:00:0022.5000.00.083409.370464.5680.32198.6730.0141.290308.9192810.13575399.936-20.2260.014451.78424.2526.72959-4.08673-8.773518201811
72018-01-01 03:30:0022.5040.00.082409.370464.1690.32398.6600.0140.965334.8302730.06593699.275-23.5980.013451.78424.3026.72959-4.08673-8.775097201811
82018-01-01 04:00:0022.7280.00.080416.003466.8170.32598.6470.0000.8772.3830670.05757099.851-17.0880.016451.78424.5246.72959-3.86734-8.853046201811
92018-01-01 04:30:0022.6670.00.080416.003466.2991.04998.6780.0000.55641.4993170.086065100.000-26.1400.014451.78424.7476.72959-3.89366-8.831906201811
TIMESTAMP_STARTTA_F_MDSSW_IN_POTSW_IN_F_MDSLW_IN_FLW_IN_JSB_FVPD_ERAPA_ERAP_FWS_FWDUSTARRHNETRADPPFD_INCO2_F_MDSTS_F_MDS_1LE_F_MDSH_F_MDSNPP_DT_VUT_USTAR50YearMonthDoY
350302019-12-31 19:00:0028.7470.00.046421.850475.7055.86798.4230.0001.81387.0227720.05028683.458-25.4190.010379.11825.883-10.7472002.80983-9.477374201912365
350312019-12-31 19:30:0028.0270.00.057421.850472.0495.44298.4890.0001.49785.5611120.14554487.944-25.6620.010385.20925.838-18.2109008.70377-9.264729201912365
350322019-12-31 20:00:0027.8460.00.056422.525469.0995.01798.5540.0531.564108.9945650.06916186.451-15.7170.009383.53525.728-2.664750-0.15918-9.211865201912365
350332019-12-31 20:30:0027.4980.00.054422.525467.7344.50398.6160.0533.01178.2945180.17440689.191-53.6870.010398.96625.619-2.530540-3.91131-9.110349201912365
350342019-12-31 21:00:0026.6640.00.060420.736463.8233.98998.6770.0212.37678.4518540.21833595.202-24.8230.011409.78025.68416.530700-9.18897-8.866398201912365
350352019-12-31 21:30:0026.2720.00.059420.736458.5333.82498.7030.0212.02218.5486740.29138393.141-18.6250.042401.85725.7495.916690-3.61120-8.752585201912365
350362019-12-31 22:00:0025.4820.00.057424.043452.2383.65998.7280.1751.23812.2198580.12648795.151-24.1620.012389.17525.6406.288130-3.64064-8.523969201912365
350372019-12-31 22:30:0025.1680.00.055424.043450.6503.43098.7260.1750.7592.8319470.16085397.314-17.3870.012390.32625.5316.421760-3.69184-8.433542201912365
350382019-12-31 23:00:0024.9000.00.062420.047448.5743.20098.7230.0540.47816.5291040.12498398.087-17.1200.018397.24225.4536.451640-3.47510-8.355394201912365
350392019-12-31 23:30:0024.6600.00.084420.047446.7052.83798.6990.0540.685192.4644370.12148498.762-20.4520.012398.45025.3750.971293-4.73520-8.283524201912365